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Inverse estimation of the particle size distribution using the fruit fly optimization algorithm

APPLIED THERMAL ENGINEERING 2015年 88卷 306-314页

作者： He, Zhenzong Qi, Hong Yao, Yuchen Ruan, Liming Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China

The fruit fly optimization algorithm (FOA) is applied to retrieve the particle size distribution (PSD) for the first time. The direct problems are solved by the modified Anomalous Diffraction Approximation (ADA) and the Lambert Beer Law. Firstly, three commonly used monomodal PSDs, i.e. the Rosin -Rammer (R-R) distribution, the normal (N-N) distribution and the logarithmic normal (L-N) distribution, and the bimodal Rosin-Rammer distribution function are estimated in the dependent model. All the results show that the FOA can be used as an effective technique to estimate the PSDs under the dependent model. Then, an optimal wavelength selection technique is proposed to improve the retrieval results of bimodal PSD. Finally, combined with two general functions, i.e. the Johnson's S-B (J-S-B)) function and the modified beta (M-beta) function, the FOA is employed to recover actual measurement aerosol PSDs over Beijing and Hangzhou obtained from the aerosol robotic network (AERONET). All the numerical simulations and experiment results demonstrate that the FOA can be used to retrieve actual measurement PSDs, and more reliable and accurate results can be obtained, if the J-S-B function is employed. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： fruit fly optimization algorithm Aerosol Particle size distribution Inverse problem

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A new type bionic global optimization: Construction and application of modified fruit fly optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2015年第9期229卷 1614-1621页

作者： Qi Qisong Xu Gening Fan Xiaoning Wang Jun Taiyuan Univ Sci & Technol Dept Mech Engn Taiyuan Shanxi Peoples R China

fruit fly optimization algorithm, which is put forward through research on the act of foraging and observing groups of fruit flies, has some merits such as simplified operation, strong robustness, easy to parallel computing, and fast convergence rate;it could solve the bottlenecks of traditional intelligent optimization algorithms on precocity and low convergence speed effectively. fruit fly optimization algorithm is applied to almost all the numerical optimization problems and is very useful in engineering applications. When the design variable is negative, traditional fruit fly optimization algorithm is not qualified for the extraordinarily slow convergence rate during the late stage of calculation and easy to be trapped in local optimum. Because of the defects of classical fruit fly optimization algorithm, a new coding method of the process of optimization is improved by this article, so the design variables could be searched toward the direction. In addition, a novel bionic global optimizationfruit fly optimization algorithm of learningis proposed by introducing the concept of study. This article tries to apply fruit fly optimization algorithm of learning to compare calculations;therefore, four classical test functions and two engineering problems are performed. It turned out that not only does fruit fly optimization algorithm of learning inherit the advantages of fruit fly optimization algorithm, but has a strong learning ability. The introduction of study ability into fruit fly optimization algorithm notably improves the efficiency and capability of optimization;it has characteristics of fast convergence rate and fast speed of approaching the global optimum solutions. fruit fly optimization algorithm of learning has the ability to solve practical problems, and its engineering prospect is promising.

关键词： fruit fly optimization algorithm numerical optimization fruit fly optimization algorithm of learning study

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A cloud model based fruit fly optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2015年第Nov.期89卷 603-617页

作者： Wu, Lianghong Zuo, Cili Zhang, Hongqiang Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Hunan Peoples R China

fruit fly optimization algorithm (FOA) is a new global optimization algorithm inspired by the foraging behavior of fruit fly swarm. However, similar to other swarm intelligence based algorithms, FOA also has its own disadvantages. To improve the convergence performance of FOA, a normal cloud model based FOA (CMFOA) is proposed in this paper. The randomness and fuzziness of the foraging behavior of fruit fly swarm in osphresis phase is described by the normal cloud model. Moreover, an adaptive parameter strategy for Entropy En in normal cloud model is adopted to improve the global search ability in the early stage and to improve the accuracy of solution in the last stage. 33 benchmark functions are used to test the effectiveness of the proposed method. Numerical results show that the proposed CMFOA can obtain better or competitive performance for most test functions compared with three improved FOAs in recent literatures and seven state-of-the-arts of intelligent optimization algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Cloud model Randomness Fuzziness

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An improved fruit fly optimization algorithm and its application to joint replenishment problems

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第9期42卷 4310-4323页

作者： Wang, Lin Shi, Yuanlong Liu, Shan Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Wuhan Univ Econ & Management Sch Wuhan 430072 Peoples R China

fruit fly optimization algorithm (FOA) is one of the recent evolutionary computation approaches. This paper presents an effective and improved FOA (IFOA) for optimizing numerical functions and solving joint replenishment problems (IRPs). In the proposed IFOA, a new method of maintaining the population diversity is developed to enhance the exploration ability. fruit flies with better fitness values use vision to fly toward a new location, and the others fly randomly in initial search space based on swarm collaboration. In addition, a new parameter to avoid the acquisition of local optimal solution is introduced to implement intelligent searching. Random perturbation is added to the updated initial location to jump out of the local optimum. Comparisons are carried out using 18 benchmark functions to verify the performance of the IFOA. Experimental results show that IFOA has better comprehensive performance than the original FOA, differential evolution algorithm, and particle swarm optimization algorithm. The IFOA is also utilized to solve the typical JRPs that have been proven as non-deterministic polynomial hard problems. Comparative examples reveal that the proposed IFOA can find better solutions than the current best algorithm;thus, it is a potential tool for various complex optimization problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： fruit fly optimization algorithm Swarm collaboration Random perturbation Function optimization Joint replenishment problem

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PID control parameters optimize based on an immune fruit fly optimization algorithm

PID control parameters optimize based on an immune fruit fly...

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第28届中国控制与决策会议

作者： Juan Song Huan Pan Ningxia University School of Physics and Electrical Information ScienceNingxia Key Laboratory of Intelligent Sensing for Desert Information

ISBN: (纸本)9781467397155

The traditional Ziegler-Nichols(Z-N) method usually fails to achieve the best control performance for tuning PID ***,this paper proposed an immune fly fruit optimization algorithm(IFOA) with the error performance criterion of ITAE as fitness function for the PID parameters ***,the proposed algorithm selected the best fruit flies for immune vaccines in the osphresis search ***,it introduced the immune vaccination and immune selection mechanism in the visual search mode,so as to avoid fly fruit optimization algorithm(FOA) falling into premature,and to overcome the artificial immune algorithm(AIA) shortcomings in the cumbersome and inefficient ***,test the performance of the hybrid algorithm with four benchmarks,and apply it in PID parameters *** results show that the IFOA has fast convergence,good stability and higher precision,and also prove the feasibility and effectiveness in PID control parameter optimization.

关键词： fruit fly optimization algorithm Artificial immune algorithm PID controller Parameters optimization

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A Survey on fruit fly optimization algorithm 11

A Survey on Fruit Fly Optimization Algorithm

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2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)

作者： Iscan, Hazim Gunduz, Mesut Selcuk Univ Fac Engn Dept Comp Engn Konya Turkey

ISBN: (纸本)9781467397216

In this study one of the recent swarm optimization algorithms namely fruit fly optimization algorithm (FOA) and some of its variants are investigated. FOA was suggested by PAN in 2011. It is a fast, easy to code and easy to understand metaheuristic algorithm having an effective search capability. Despite the advantages of the algorithm, FOA has some deficiencies which were encountered by the researchers during the implementations as shown in literature. To overcome the deficiencies of the algorithm various enhancements were applied by the researchers. This study represents and explains the FOA at first. Then, the improvements which were made on FOA are illustrated, and their implementations are given by examples. The study is concluded by illustrating the evolution and the hybrid variants of FOA.

关键词： fruit fly optimization algorithm metaheuristic improvement of fruit fly modified fruit fly FOA FFOA

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A Binary fruit fly optimization algorithm to Solve the Set Covering Problem 15th

A Binary Fruit Fly Optimization Algorithm to Solve the Set C...

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15th International Conference on Computational Science and Its Applications (ICCSA)

作者： Crawford, Broderick Soto, Ricardo Torres-Rojas, Claudio Pena, Cristian Riquelme-Leiva, Marco Misra, Sanjay Johnson, Franklin Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ San Sebastian Santiago Chile Univ Cent Chile Santiago Chile Univ Autonoma Chile Santiago Chile Univ Cient Sur Lima Peru Covenant Univ Ota Nigeria Univ Playa Ancha Valparaiso Chile Univ Diego Portales Escuela Ingn Ind Santiago Chile

ISBN: (纸本)9783319214108;9783319214092

The Set Covering Problem (SCP) is a well known NP-hard problem with many practical applications. In this work binary fruit fly optimization algorithms (bFFOA) were used to solve this problem using different binarization methods. The bFFOA is based on the food finding behavior of the fruit flies using osphresis and vision. The experimental results show the effectiveness of our algorithms producing competitive results when solve the benchmarks of SCP from the OR-Library.

关键词： Set Covering Problem fruit fly optimization algorithm Metaheuristics Combinatorial optimization problem

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An Improved fruit fly optimization algorithm and Its Application

An Improved Fruit Fly Optimization Algorithm and Its Applica...

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2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2015)

作者： Shi Hui Shu San Ye Zhu Yi Harbin Institute of Technology Control and Simulation Center

ISBN: (纸本)9781510806481

fruit fly optimization algorithm is a new swarm intelligent algorithm proposed in recent years and has been concerned for its few parameters and high computational efficiency. However, the application of the algorithm is limited for unstable optimization capability. To solve that question, an improved fruit fly optimization algorithm is proposed in this paper. Some factors affecting the performance of the algorithm are improved. The improved algorithm are proved by function optimization and applied to the analog circuit fault diagnosis.

关键词： fruit fly optimization algorithm intelligent algorithm SVM analog circuit fault diagnosis

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A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem

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KNOWLEDGE-BASED SYSTEMS 2014年第0期57卷 95-103页

作者： Zheng, Xiao-long Wang, Ling Wang, Sheng-yao Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, a novel fruit fly optimization algorithm (nFOA) is proposed to solve the semiconductor final testing scheduling problem (SFTSP). First, a new encoding scheme is presented to represent solutions reasonably, and a new decoding scheme is presented to map solutions to feasible schedules. Second, it uses multiple fruit fly groups during the evolution process to enhance the parallel search ability of the FOA. According to the characteristics of the SFTSP, a smell-based search operator and a vision-based search operator are well designed for the groups to stress exploitation. Third, to simulate the information communication behavior among fruit flies, a cooperative search process is developed to stress exploration. The cooperative search process includes a modified improved precedence operation crossover (IPDX) and a modified multipoint preservative crossover (MPX) based on two popular structures of the flexible job shop scheduling. Moreover, the influence of the parameter setting is investigated by using Taguchi method of design-of-experiment (DOE), and suitable values are determined for key parameters. Finally, computational tests results with some benchmark instances and the comparisons to some existing algorithms are provided, which demonstrate the effectiveness and the efficiency of the nFOA in solving the SFTSP. (C) 2013 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Smell-based search Vision-based search Cooperative search Semiconductor final testing scheduling problem

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Comment and improvement on "A new fruit fly optimization algorithm: Taking the financial distress model as an example"

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KNOWLEDGE-BASED SYSTEMS 2014年 59卷 159-160页

作者： Dai, Hongde Zhao, Guorong Lu, Jianhua Dai, Shaowu Naval Aeronaut & Astronaut Univ Dept Control Engn Yantai 264001 Peoples R China

A new fruit fly optimization algorithm (FOA) has been introduced in a recent paper published in Knowledge-Based Systems by Pan (2012) [1], which is much simpler and more robust compared with the normal optimization algorithm such as genetic algorithm, ant colony optimization and particle swarm optimization. However, it is found that a improvement is required, the smell concentration judgment value S is non-negative in Ref. [1], which will restrict the application of FOA in some problem, an improvement is proposed in this letter, comparison between the traditional FOA and the improved FOA have been done by simulation, results show the effectiveness of the improved algorithm. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm optimization problem Genetic algorithm Ant colony optimization Particle swarm optimization

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